Planar-beam, light detection and ranging system

ABSTRACT

A planar-beam, light detection and ranging (PLADAR) system can include a laser to output a laser beam and a collimator configured to collimate the laser beam axially to emit a planar beam from the laser. The PLADAR system can further include a detector to detect reflected light based on the planar beam being reflected from external surfaces of target objects.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/446,953, titled “PLANAR-BEAM, LIGHT DETECTION AND RANGING SYSTEM”,and filed on Mar. 1, 2017; which claims the benefit of priority to U.S.Provisional Application No. 62/303,013, titled “PLANAR-BEAM, LIGHTDETECTION AND RANGING SYSTEM,” and filed on Mar. 3, 2016; theaforementioned applications being hereby incorporated by reference intheir respective entireties.

BACKGROUND

Light detection, and ranging (LIDAR or LADAR) systems utilize a numberof laser beams to detect reflectance or backscatter from the laser beamsto map surface features or for remote sensing. For typical LIDARsystems, each beam is precisely configured with a dedicatedphotodetector that detects the reflectance and/or backscatter from thatparticular beam. As the beam count increases, so do cost and spacerequirements for the individual lasers and photodetectors.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure herein is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings in which likereference numerals refer to similar elements, and in which:

FIG. 1 is a diagram illustrating an example planar-beam, light detectionand ranging (PLADAR) system, as described herein;

FIG. 2 is a block diagram illustrating an example autonomous vehicle(AV) including a PLADAR system, as described herein;

FIG. 3 is a flow chart describing an example method of processing PLADARdata, according to one or more examples described herein;

FIGS. 4A and 4B are a flow chart describing example methods ofconfiguring a PLADAR system, as described herein; and

FIG. 5 is a block diagram illustrating an example computing system uponwhich examples described herein may be implemented.

DETAILED DESCRIPTION

Current LIDAR technology involves fixed-beam LIDAR systems that includelaser sources, scanners, optical systems (e.g., beam splitters and/orcollimators), and photodetectors. For example, cutting edge LIDARsystems can include pulse rates on the order of one million pulses persecond producing a detailed point cloud map of an autonomous vehicle'ssurroundings at ranges upwards of one hundred-plus meters. These LIDARsystems require precision pulse sequencing for the laser beams formultiple reasons, such as power constraints, sampling and/or processingconstraints, and the like. When using typical LIDAR systems forautonomous vehicles traveling on public roads, operational speed may belimited by the nature of the beam pattern produced by the LIDAR system.For example, in order to ensure safety for an autonomous vehicletraveling at low speeds over public roads, a LIDAR system may requireseveral separate beams to readily detect potential hazards withsufficient granularity to decelerate, maneuver, and/or stop theautonomous vehicle accordingly. When the autonomous vehicle travels athigh speeds (e.g., 60 mph, 75 mph, etc.), in order to achieve the samegranularity for potential hazards in order to safely react, decelerate,and/or stop the autonomous vehicle, a fixed-beam LIDAR system mayrequire well over seventy separate beams.

Increasing the number of fixed beams places additional requirements fora LIDAR system. For example, the LIDAR system will require more power,greater processing capability, larger or more sensitive photodetectorand receiving equipment, constrained optics, and generally greaterweight and more space. Furthermore, cost and waste quickly become anissue when increasing the number of fixed-beams, since the beam patternfor the fixed-beam LIDAR system must be tuned for a maximum operationalspeed of the autonomous vehicle. If autonomous vehicles are to operatesafely with LIDAR technology on public highways at high speed, thenalternative arrangements may be necessary to avoid spiraling costs,wasted power, additional equipment, and increased processingrequirements.

To address many of the shortcomings of fixed-beam LIDAR systems, aplanar-beam, light detection and ranging (PLADAR) system is provided.The PLADAR system can include a laser scanner that emits a planar-beam,and a detector array to detect reflected light (e.g., backscatter) fromthe planar beam. In some aspects, the laser scanner can include acollimation component that collimates a laser beam generated by thelaser scanner into the planar beam. The laser scanner can utilize asingle laser collimated on an axis (e.g., a vertical axis) to generatethe planar beam, which can extend from the laser scanner approximatelytriangularly as opposed to linearly. According to certainimplementations, the laser scanner can include a fiber laser thatgenerates the laser beam for axial collimation. Fiber lasers can offervibrational stability, ideal optical quality, and compact size inaddition to other desirable qualities. However, virtually any type oflaser with appropriate emission characteristics may be used, such ascertain types of gas lasers, excimer lasers, dye lasers, other forms ofsolid state lasers, semi-conductor based lasers, metal vapor lasers,etc. utilizing continuous wave or pulsed emissions. For autonomousvehicle applications, wavelengths on the order of 1000 nanometers (nm)(e.g., 1200-1550 nm) corresponding to the near-infrared spectral rangemay be optimal for health and safety reasons.

In many examples, the detector array of the PLADAR system can include atleast one set of photodetectors, such as one or more linear rows ofphotodetectors, which can be included on a circuit board of the PLADARsystem. The circuit board can include a number of adjustment componentsfor the photodetector array(s) to calibrate the photodetectors inconcert. In some aspects, the PLADAR system can further include anadjustment controller to dynamically adjust the adjustment components tooptimally configure the row(s) of photodetectors in response to acommand signal. The command signal may be generated by a calibrationsystem pre-implementation, or dynamically when the PLADAR system is inuse. Additionally or alternatively, the adjustment components can betuned manually by a user or technician when calibrating thephotodetector array to the planar beam.

Accordingly to examples described herein, the PLADAR system can beimplemented on an autonomous vehicle to provide sensor data to anon-board data processing system of the autonomous vehicle. The PLADARsystem can include an analog-to-digital converter (ADC) chain coupled tothe photodetector array. In certain implementations, the ADC chain cangenerate output from all of the photodetectors simultaneously, and theoutputted data can be processed (e.g., by the on-board data processingsystem of the autonomous vehicle) accordingly. In such implementations,the pulse rate of the planar beam can be significantly reduced comparedto fixed-beam LIDAR systems. For example, when fine granularity isdesired, instead of transmitting one hundred or so beams (e.g., with˜0.15° beam spacing), examples described herein can transmit a single(or multiple) beam planes with the same or similar data quality at ˜1/100th the pulse rate.

Among other benefits, the examples described herein achieve a technicaleffect of providing an alternative to increasingly expensive, complex,and tediously calibrated LIDAR systems. A PLADAR system can maintain orincrease data quality while reducing cost and complexity, which areincreasing concerns in autonomous vehicle technology and currentlyfunction as hindrances in the rollout of autonomous vehicles for commonuse.

As used herein, a PLADAR system implements remote sensing using planarbeams as opposed to linear beams. “PLADAR” is used herein to representany light detection and ranging system that uses two-dimensional beamplanes for remote sensing.

As used herein, a computing device refer to devices corresponding todesktop computers, cellular devices or smartphones, personal digitalassistants (PDAs), field programmable gate arrays (FPGAs), laptopcomputers, tablet devices, television (IP Television), etc., that canprovide network connectivity and processing resources for communicatingwith the system over a network. A computing device can also correspondto custom hardware, in-vehicle devices, or on-board computers, etc. Thecomputing device can also operate a designated application configured tocommunicate with the network service.

One or more examples described herein provide that methods, techniques,and actions performed by a computing device are performedprogrammatically, or as a computer-implemented method. Programmatically,as used herein, means through the use of code or computer-executableinstructions. These instructions can be stored in one or more memoryresources of the computing device. A programmatically performed step mayor may not be automatic.

One or more examples described herein can be implemented usingprogrammatic modules, engines, or components. A programmatic module,engine, or component can include a program, a sub-routine, a portion ofa program, or a software component or a hardware component capable ofperforming one or more stated tasks or functions. As used herein, amodule or component can exist on a hardware component independently ofother modules or components. Alternatively, a module or component can bea shared element or process of other modules, programs or machines.

Some examples described herein can generally require the use ofcomputing devices, including processing and memory resources. Forexample, one or more examples described herein may be implemented, inwhole or in part, on computing devices such as servers, desktopcomputers, cellular or smartphones, personal digital assistants (e.g.,PDAs), laptop computers, printers, digital picture frames, networkequipment (e.g., routers) and tablet devices. Memory, processing, andnetwork resources may all be used in connection with the establishment,use, or performance of any example described herein (including with theperformance of any method or with the implementation of any system).

Furthermore, one or more examples described herein may be implementedthrough the use of instructions that are executable by one or moreprocessors. These instructions may be carried on a computer-readablemedium. Machines shown or described with figures below provide examplesof processing resources and computer-readable mediums on whichinstructions for implementing examples disclosed herein can be carriedand/or executed. In particular, the numerous machines shown withexamples of the invention include processor(s) and various forms ofmemory for holding data and instructions. Examples of computer-readablemediums include permanent memory storage devices, such as hard drives onpersonal computers or servers. Other examples of computer storagemediums include portable storage units, such as CD or DVD units, flashmemory (such as carried on smartphones, multifunctional devices ortablets), and magnetic memory. Computers, terminals, network enableddevices (e.g., mobile devices, such as cell phones) are all examples ofmachines and devices that utilize processors, memory, and instructionsstored on computer-readable mediums. Additionally, examples may beimplemented in the form of computer-programs, or a computer usablecarrier medium capable of carrying such a program.

System Description

FIG. 1 is a block diagram illustrating an example planar-beam, lightdetection and ranging (PLADAR) system, as described herein. The PLADARsystem 100 can include a PLADAR scanner and optics 114 that generate atwo-dimensional beam plane 118. The scanner/optics 114 can include asingle laser source (or multiple laser sources) that generates a laserbeam. In certain aspects, the laser source of the beam plane 118 can bea fiber laser emitting in the near to mid infrared spectral range. Theoptics of the scanner/optics 114 can include a number mirrors and/or acollimation component that collimates the laser beam axially to form thebeam plane 118. The collimation component can include a number oflenses, spatial filters, mirrors, fiber optics, and/or gratings, whichcan filter, amplify, and/or narrow a resultant planar beam 118.Accordingly, the collimation component of the scanner/optics 114collimates the laser beam on a single axis to generate the beam plane118.

In many aspects, the PLADAR system 100 can include a photodetector array119 including a number of individual photodetectors. According toexamples described herein, the photodetector array 119 can comprise alinear arrangement (e.g., as a one or more linear rows ofphotodetectors) to correlate with the beam plane 118 emitted by thePLADAR system 100. For example, the individual photodetectors can beincluded and calibrated on a circuit board to be aligned with the beamplane 118. Furthermore, the photodetector array 119 can include a numberof adjustable components 123 (e.g., calibration screws) that can allowfor straightforward calibration of the photodetector array 119 with thebeam plane 118. In some aspects, the photodetector array 119 can includea sufficient number of individual photodetectors (e.g., tens tohundreds) for generating sensor data with sufficient granularity todetect any possible road hazards (e.g., objects with size on the orderof feet or inches) for operating an autonomous vehicle on public roads.In such aspects, the photodetector array 119 can include as many or morephotodetectors as current or future state of the art fixed-beam LIDARsystems.

In various implementations, the photodetector array 119 can includemultiple linear arrangements of photodetectors, and/or interleaveddetectors across the multiple linear arrangements. For example, thephotodetector array 119 can include two or more lines of photodetectorsto take advantage of the beam spread of the beam plane 118. Invariations, the nature of the beam plane 118 can allow for any suitablearrangement for the photodetector array 119, such as separate staggeredphotodetector lines of varying lengths, a wider central arrangement, anarrower central arrangement, and the like. Thus, in addition to asingle linear row of photodetectors, the photodetector array can includea plurality of photodetector rows aligned in a manner corresponding tothe beam plane 118.

The photodetector array 119 detects reflection and/or backscatter 126from the beam plane 118, and a timing component 135 is utilized toperform ranging operations for the PLADAR system 100. Accordingly, thePLADAR system 100 actively transmits the beam plane 118, light from thebeam plane 118 is reflected off objects and surfaces, and thisreflection/backscatter 126 is detected by the individual receivers ofthe photodetector array 119. Data from the detected light is preciselytimed to perform the ranging operations (e.g., dynamic calculations ofdistance to each surface) and generate a dynamic point cloud map of thesituational environment of the PLADAR system 100.

Furthermore, LIDAR systems sequence the individual beams and detectorsin order to decrease power and processing loads, which can constraindata quality. The PLADAR system 100 can utilize a single beam plane 118at a single pulse rate, which may be increased or decreased accordinglydepending on the situational environment (e.g., a crowded cityenvironment, a rural road with little or no traffic, etc.). In certainaspects, PLADAR sensor data 129 from the photodetectors of thephotodetector array 119 may be sampled simultaneously from allphotodetectors by a local or external data processor 130. Thus, in someimplementations, the data processor 130 can be included as a componentof the PLADAR system 100. In other implementations, the data processor130 may be remote, and/or can be included as a part of, for example, anon-board data processing system of an autonomous vehicle.

Each detector of the photodetector array 119 can include ananalog-to-digital converter (ADC), which converts the detected lightsignal from the reflection/backscatter 126 of the beam plane 118 into adigital signal for processing. Accordingly, in many examples, thephotodetector array 119 can include an ADC chain, similar to certainLIDAR systems. The combined data from the ADC chain (i.e., PLADAR sensordata 129) can be sampled by the data processor 130 (e.g., simultaneouslyor near-simultaneously for each pulse) to generate the point cloud mapof the situational environment. Feedback 132 can be provided by the dataprocessor 130 to a PLADAR controller 150, or adjustment controller,which can make adjustments to the configurable parameters of the PLADARsystem 100.

According to examples described herein, the individual detectors of thephotodetector array 119 can be adjusted in concert using the adjustablecomponents 123. In certain examples, the photodetector array 119 can bepre-calibrated and aligned with the beam plane 118 during themanufacturing process. Additionally or alternatively, when amisalignment is detected (e.g., by the data processor 130), thephotodetector array 119 may be manually calibrated during servicing.Additionally or alternatively still, the feedback 132 provided by thedata processor 130 can indicate the misalignment, and can be processedby the PLADAR controller 150. The PLADAR controller 150 can determine anumber of adjustments based on the feedback 132, and can utilize theadjustable components 123 to re-calibrate the photodetector array 119automatically and on the fly.

In certain aspects, the PLADAR controller 150 can further operate thePLADAR motor 120, which can, for example, control a rotational rate ofthe PLADAR system 100. The PLADAR controller can further control thetiming component 135 and the PLADAR scanner/optics 114 to increase ordecrease the pulse rate when, for example, finer granularity in thegenerated point cloud is needed (e.g., in pedestrian rich environments).Generally, however, the pulse rate of the beam plane 118 can be far less(e.g., 100× less) than those of typical LIDAR systems, since the PLADARsystem 100 utilizes a single light source.

Other arrangements are contemplated. For example, the PLADARscanner/optics 114 can generate the beam plane 118 along with one ormore linear beams having dedicated detectors. As another example, thePLADAR scanner/optics 114 can generate multiple beam planes 118. Theembodiment illustrated in FIG. 1 shows a rotational PLADAR system 100operated by a PLADAR motor 120. However, example PLADAR systems 100described herein can include a scanning motor that uses a beam plane 118to scan a certain directional aspect (e.g., directly in front of anautonomous vehicle). Further, the beam plane 118 is axially collimatedon a single axis, and can be collimated vertically, horizontally, in aslanted manner (as shown), and can provide almost any desired verticalfield of view (e.g., a 45° VFOV at 25 meters).

FIG. 2 is a block diagram illustrating an example autonomous vehicleincluding a PLADAR system, as described herein. The PLADAR system 205 ofthe autonomous vehicle (AV) 200 can provide PLADAR data 202 to anon-board data processing system 210 of the autonomous vehicle 200. Insome examples, the PLADAR system 205 can comprise a light source (e.g.,a laser), a photodetector, scanner components (e.g., which can includeone or more lens(es), mirror(s), motor(s), actuator(s), etc.), andcircuitry to couple to various components of the autonomous vehicle 200.The data processing system 210 can utilize the PLADAR data 202 to detectthe situational conditions of the autonomous vehicle 200 as the AV 100travels along a current route. For example, the data processing system210 can identify potential obstacles or road hazards—such aspedestrians, bicyclists, objects on the road, road cones, road signs,animals, etc.—in order to enable an AV control system 220 to reactaccordingly.

In certain implementations, the data processing system 210 can utilizesub-maps 233 stored in a database 230 of the autonomous vehicle 200 inorder to perform localization and pose operations to determine a currentlocation and orientation of the autonomous vehicle 200 in relation to agiven region (e.g., a city). The sub-maps 233 can comprise previouslyrecorded sensor data, such as stereo camera data, radar maps, and/orpoint cloud LIDAR maps that enable the data processing system 210 tocompare the PLADAR data 202 from the PLADAR system 205 with a currentsub-map 234 to identify such obstacles and potential road hazards inreal time. The data processing system 210 can provide the processedsensor data 213—identifying such obstacles and road hazards—to AVcontrol system 220, which can react accordingly by operating thesteering, braking, and acceleration systems 225 of the autonomousvehicle 200.

In some examples, the autonomous vehicle 200 further includes a numberof stereo cameras 260 that generate dynamic image data 262 of theautonomous vehicle's 200 surroundings. For example, the autonomousvehicle 200 can include stereo cameras 260 with fields of view showing a360° panorama (or forward and rearward directions) of the autonomousvehicle 200. The on-board data processing system 210 can further processthe dynamic image data 262 to identify features or potential hazardsalong the current route traveled. The processed data 213 can includeprocessed image data from the stereo cameras 260, which can be utilizedby the AV control system 220 to perform low level maneuvering.

In many implementations, the AV control system 220 can receive adestination 219 from, for example, an interface system 215 of theautonomous vehicle 200. The interface system 215 can include any numberof touch-screens, voice sensors, mapping resources, etc. that enable apassenger 239 to provide a passenger input 241 indicating thedestination 219. For example, the passenger 239 can type the destination219 into a mapping engine 275 of the autonomous vehicle 200, or canspeak the destination 219 into the interface system 215. Additionally oralternatively, the interface system 215 can include a wirelesscommunication module that can connect the autonomous vehicle 200 to anetwork 280 to communicate with a backend transport arrangement system290 to receive invitations 282 to service a pick-up or drop-off request.Such invitations 282 can include destination 219 (e.g., a pick-uplocation), and can be received by the autonomous vehicle 200 as acommunication over the network 280 from the backend transportarrangement system 290. In many aspects, the backend transportarrangement system 290 can manage routes and/or facilitatetransportation for users using a fleet of autonomous vehicles throughouta given region. The backend transport arrangement system 290 can beoperative to facilitate passenger pick-ups and drop-offs to generallyservice pick-up requests, facilitate delivery such as packages, food, oranimals, and the like.

Based on the destination 219 (e.g., a pick-up location), the AV controlsystem 220 can utilize the mapping engine 275 to receive route data 232indicating a route to the destination 219. In variations, the mappingengine 275 can also generate map content 226 dynamically indicating theroute traveled to the destination 219. The route data 232 and/or mapcontent 226 can be utilized by the AV control system 220 to maneuver theautonomous vehicle 200 to the destination 219 along the selected route.For example, the AV control system 220 can dynamically generate controlcommands 221 for the autonomous vehicle's steering, braking, andacceleration system 225 to actively drive the autonomous vehicle 200 tothe destination 219 along the selected route. Optionally, the mapcontent 226 showing the current route traveled can be streamed to theinterior interface system 215 so that the passenger(s) 239 can view theroute and route progress in real time.

In many examples, while the AV control system 220 operates the steering,braking, and acceleration systems 225 along the current route on a highlevel, and the processed data 213 provided to the AV control system 220can indicate low level occurrences, such as obstacles and potentialhazards to which the AV control system 220 can make decisions and react.For example, the processed data 213 can indicate a pedestrian crossingthe road, traffic signals, stop signs, other vehicles, road conditions,traffic conditions, bicycle lanes, crosswalks, pedestrian activity(e.g., a crowded adjacent sidewalk), and the like. The AV control system220 can respond to the processed data 213 by generating control commands221 to reactively operate the steering, braking, and accelerationsystems 225 accordingly.

According to examples described herein, the autonomous vehicle 200 caninclude a PLADAR controller 235 to receive feedback data 223 from thedata processing system 210 in order to configure various adjustableparameters of the PLADAR system 205. The feedback data 223 can includeinformation indicating data quality, such as errors or uncertainty inthe data from certain individual photodetectors, which can beextrapolated by the PLADAR controller 235 to determine a number ofadjustment commands 237 for the PLADAR system 205 that can correct theerror(s). For example, the PLADAR controller 235 can identify a patternin the feedback data 223 indicating a misalignment of the photodetectorarray with respect to the PLADAR beam 207. The PLADAR controller 235 canidentify the misalignment and generate the adjustment commands 237 forexecution on the adjustable components of the photodetector array tore-calibrate the PLADAR system 205. As discussed herein, the adjustmentcommands 237 can be executed on the adjustable components dynamically asthe autonomous vehicle 200 travels along a current route, or duringgarage servicing of the autonomous vehicle 200.

Additionally or alternatively, the feedback data 223 can includerequests from the data processing system 210 for the PLADAR controller235 to configure the PLADAR system 205 for increased or decreasedgranularity. For example, the data processing system 210 can identify asubstantial decrease in potential hazards (e.g., when the autonomousvehicle 200 leaves a city and enters open rural road with littletraffic). The feedback data 223 can include a request to save power insuch conditions by decreasing the pulse rate and/or scan rate of thePLADAR system 205. Accordingly, in some aspects, the adjustment commands237 can be generated by the PLADAR controller 235 to adjust a rotationalparameter 209 (e.g., decrease a rotational rate) and/or decrease thepulse rate of the PLADAR beam 207—thereby enabling a decrease in samplerate by the data processing system 210.

Conversely, the on-board data processing system 210 can identify anincrease in potential hazards (e.g., entering an area of increasedpedestrian activity) or an increased probability of experiencing hazards(e.g., when traveling at high speeds), and request that the PLADARcontroller 235 generate adjustment commands 237 to increase the samplerate. Such commands 237 can be executed on the adjustable parameters ofthe PLADAR system 205 to increase a pulse rate of the PLADAR beam 207and/or increase the rotational rate—thereby enabling the data processingsystem 210 to increase the sample rate and bolster point cloudgranularity.

Methodology

FIG. 3 is a flow chart describing an example method of processing PLADARdata, according to one or more examples described herein. In the belowdescription of FIG. 3, reference may be made to like featuresrepresented by reference characters from FIGS. 1 and 2. Furthermore, themethod described with respect to FIG. 3 may be performed by an exampledata processor 130 shown and described with respect to FIG. 1, or anon-board data processing system 230 shown and described with respect toFIG. 2. Referring to FIG. 3, the data processor 130 can sample data fromeach detector of the PLADAR system 100 simultaneously (300). Forexample, the data processor 130 can monitor each ADC of an ADC chaincoupled to the photodetector array 119. For each beam plane 118 pulse,return light signals (e.g., reflection/backscatter 126) can be receivedby the photodetector array 119. Each detector can include an ADC thatconverts the detected light signal into a digital signal, and a timingcomponent 135 can be utilized by the data processor 130 to preciselyperform ranging for each ADC of the ADC chain and for every beam plane118 pulse.

The data processor 130 (e.g., of an autonomous vehicle 200) can processthe PLADAR data 129 to perform ranging and identify potential hazards(305). For example, the data processor 130 can be programmed to identifyaspects of the autonomous vehicle's situational environment that causesthe autonomous vehicle 200 to operate safely on public roads. Suchaspects can include pedestrians, bicyclists, hazardous objects on theroad (e.g., rocks), stop lights, signs, other vehicles, and the like.The processor 130 can identify such aspects by, for example, comparingthe PLADAR data 129 to a stored sub-map including prerecorded data onthe same current route, as described with respect to FIG. 2. In manyimplementations, the data processor 130 transmits the processed sensordata 213 to an AV control system 220, which can control the autonomousvehicle's 200 steering braking, and acceleration systems 225 to makedecisions and react to each processed object for low level maneuvering(310). Additionally, the AV control system 220 can further utilizedynamic image data 262 from a stereo camera system 260 of the autonomousvehicle 200 for low level maneuvering.

In certain implementations, the data processor 130 can identify, in thePLADAR data 129, a misalignment between the photodetector array 119 andthe beam plane 118 (315). As an example, the PLADAR data 129 canindicate unreliable data for the top detectors and the bottom detectors,which can indicate a diagonal misalignment of the photodetector array118. In some aspects, the data processor 130 can determine the nature ofthe misalignment based on the sampled PLADAR data 129. In other aspects,the data processor 130 can generally identify the error in the data, andgenerate feedback 132 requesting the PLADAR controller 150 to perform adiagnostics test. In either aspect, the data processor 130 can generatefeedback 132 indicating the misalignment (320), and transmit thefeedback to the PLADAR controller 150 to re-calibrate the photodetectorarray 119 to the planar beam 118 (325).

According to some examples, the data processor 130 can determine acondition change in the situational environment of the autonomousvehicle 200 (330). As an example, the data processor 130 can identifythat the autonomous vehicle 200 is traveling at higher speeds, and thatmore detailed data from a forward direction of the autonomous vehicle200 is desired. The data processor 130 can generate a request to adjustthe PLADAR system 100 configurations to, for example, increase a pulserate, scan rate, detector sensitivity, and/or a laser intensity toincrease the data quality (335). Conversely, to optimize power andprocessing resources, in certain circumstances (e.g., low speedoperation), the data processor 130 can generate a request to decreasesuch configurable parameters when situational conditions are conduciveto such decreases (335). These requests can be transmitted to the PLADARcontroller 150 (340), which can execute adjustment commands 237 on theconfigurable components of the PLADAR system 100 accordingly.

FIGS. 4A and 4B are a flow chart describing example methods ofconfiguring a PLADAR system, as described herein. In the belowdescription of FIGS. 4A and 4B, reference may be made to like featuresrepresented by reference characters from FIGS. 1 and 2. Furthermore, themethods described with respect to FIGS. 4A and 4B may be performed by anexample PLADAR controller 150, 235 shown and described with respect toFIGS. 1 and 2. Referring to FIG. 4A, the PLADAR controller 150 canreceive feedback 132 from the data processor 130 indicating amisalignment (400). In some examples, the feedback 132 identifies thespecific misalignment (e.g., leftward, rightward, topward, downward,clockwise or counterclockwise diagonal misalignments or any combinationof the foregoing).

In other examples, the feedback 132 can include samplings of the PLADARdata 129, which the PLADAR controller 150 can analyze to identify a datapattern that describes or details the misalignment (405). For example,data from the photodetector array 119 can indicate a consistent patternof bad or unreliable data from any number of individual detectors in thearray 119. In some situations, the PLADAR controller 150 can perform aninitial set of adjustments on the adjustable components 123 to diagnosethe misalignment. In other situations, the misalignment may be readilyidentified by the PLADAR controller 150, and the calibration can be madedirectly. Accordingly, once the precise misalignment is identified, thePLADAR controller 150 can generate and execute adjustment commands 237on the adjustable components 123 of the photodetector array 119 torealign or re-calibrate the photodetector array 119 to the beam plane118 (410).

Referring to FIG. 4B, the PLADAR controller 150 can receive a requestfrom the data processor 130 to adjust PLADAR system 100 configurations(450). For example, based on changing situational conditions (e.g.,changing weather such as rain or snow, changing speed, changingenvironmental complexity or potential hazard count, etc.), the dataprocessor 130 can determine that an increased or decreased pulse rate(451) and/or scan rate (452) is preferable. Additionally oralternatively, the data processor 130 may determine that conditionsrequire an increase in laser intensity (453) to enhance reflectance, oran increase in detector sensitivity (454). Alternatively, the dataprocessor 130 may determine that conditions are conducive to powersavings (e.g., in low speed uncrowded situations), and may request todecrease such configurations.

In any case, the PLADAR controller 150 can generate adjustment commands237 based on the requests from the data processor 130 (455). The PLADARcontroller 150 can then execute the adjustment commands 237 on therelevant components of the PLADAR system 100 to configure the PLADARsystem 100 accordingly (460). For example, the PLADAR controller 150 canexecute commands 237 on the PLADAR motor 120 to increase or decrease thescan rate (461). As another example, the PLADAR controller 150 canexecute commands 237 on the timing component 135 to increase or decreasea pulse rate of the laser (462). Further, the PLADAR controller 150 canexecute commands 237 on the laser source itself to increase or decreaselaser intensity (e.g., increase or decrease power or beam frequency)(463). Still further, in some implementations, the PLADAR controller 150can execute commands 237 on the detector array 119 to increase ordecrease detector sensitivity (464).

While the data processor 130 and PLADAR controller 150 are shown asseparate components in FIGS. 1 and 2, it is contemplated that certainembodiments can include a single component (e.g., one or more bladecomputers of an autonomous vehicle 200 that perform all of theoperations described with respect to FIG. 3 and FIGS. 4A and 4B.

Hardware Diagram

FIG. 5 is a block diagram that illustrates a computer system upon whichexamples described herein may be implemented. A computer system 500 canbe implemented on, for example, a server or combination of servers. Forexample, the computer system 500 may be implemented as part of a dataprocessing system 130, which itself may be implemented as a part of theAV's on-board data processing system 210. In the context of FIG. 1, thedata processing system 130 may be implemented with the PLADAR controller150 as a single computer system 500, or using a combination of multiplecomputer systems as described in connection with FIG. 5.

In one implementation, the computer system 500 includes processingresources 510, a main memory 520, a read-only memory (ROM) 530, astorage device 540, and a communication interface 550. The computersystem 500 includes at least one processor 510 for processinginformation stored in the main memory 520, such as provided by a randomaccess memory (RAM) or other dynamic storage device, for storinginformation and instructions which are executable by the processor 510.The main memory 520 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by the processor 510. The computer system 500 may also includethe ROM 530 or other static storage device for storing staticinformation and instructions for the processor 510. A storage device540, such as a magnetic disk or optical disk, is provided for storinginformation and instructions.

The communication interface 550 enables the computer system 500 tocommunicate with the PLADAR system 580 over a network link (e.g., awireless or wired link). In accordance with examples, the computersystem 500 receives PLADAR data 582 from the PLADAR system 580. Theexecutable instructions stored in the memory 530 can includeconfiguration instructions 522, which the processor 510 executes togenerate a set of adjustment commands 554 to configure the adjustableparameters of the autonomous vehicle's PLADAR system 580 based on thePLADAR data 582 and the situational conditions of the autonomous vehicle200.

The processor 510 is configured with software and/or other logic toperform one or more processes, steps and other functions described withimplementations, such as described by FIGS. 1 through 4B, and elsewherein the present application.

Examples described herein are related to the use of the computer system500 for implementing the techniques described herein. According to oneexample, those techniques are performed by the computer system 500 inresponse to the processor 510 executing one or more sequences of one ormore instructions contained in the main memory 520. Such instructionsmay be read into the main memory 520 from another machine-readablemedium, such as the storage device 540. Execution of the sequences ofinstructions contained in the main memory 520 causes the processor 510to perform the process steps described herein. In alternativeimplementations, hard-wired circuitry may be used in place of or incombination with software instructions to implement examples describedherein. Thus, the examples described are not limited to any specificcombination of hardware circuitry and software.

It is contemplated for examples described herein to extend to individualelements and concepts described herein, independently of other concepts,ideas or system, as well as for examples to include combinations ofelements recited anywhere in this application. Although examples aredescribed in detail herein with reference to the accompanying drawings,it is to be understood that the concepts are not limited to thoseprecise examples. As such, many modifications and variations will beapparent to practitioners skilled in this art. Accordingly, it isintended that the scope of the concepts be defined by the followingclaims and their equivalents. Furthermore, it is contemplated that aparticular feature described either individually or as part of anexample can be combined with other individually described features, orparts of other examples, even if the other features and examples make nomentioned of the particular feature. Thus, the absence of describingcombinations should not preclude claiming rights to such combinations.

What is claimed is:
 1. A planar-beam, light detection and ranging(PLADAR) system, the PLADAR system comprising: a laser to output a laserbeam; a collimator configured to collimate the laser beam on an axis toemit a planar beam laser along a beam plane; and a detector array todetect reflected light based on the planar beam being reflected fromexternal surfaces of target objects, the detector array comprising aplurality of photodetectors arranged on a circuit board such that eachof the photodetectors is aligned with the beam plane.
 2. The PLADARsystem of claim 1, wherein the laser comprises a fiber laser thatoutputs the laser beam.
 3. The PLADAR system of claim 1, wherein thecollimator comprises a set of mirrors that produce the planar beam fromthe laser beam.
 4. The PLADAR system of claim 1, wherein the collimatorcomprises a set of lenses that produce the planar beam from the laserbeam.
 5. The PLADAR system of claim 1, wherein the plurality ofphotodetectors include at least one row of photodetectors, and whereinthe circuit board includes a number of adjustment components tocalibrate each photodetector included in the at least one row ofphotodetectors.
 6. The PLADAR system of claim 5, further comprising: anadjustment controller that dynamically adjusts the adjustment componentsto configure the at least one row of photodetectors in response to acommand signal.
 7. The PLADAR system of claim 5, wherein the PLADARsystem is included on an autonomous vehicle comprising a control systemthat processes sensor data from the PLADAR system to dynamicallyidentify features as the autonomous vehicles accelerates and maneuvers.8. The PLADAR system of claim 7, further comprising: an analog todigital converter (ADC) chain coupled to the at least one row ofphotodetectors; wherein the control system of the autonomous vehiclesamples data from each ADC of the ADC chain simultaneously.
 9. Anautonomous vehicle comprising: acceleration; braking, and steeringsystems; a control system to autonomously operate the acceleration,braking, and steering systems; and a planar-beam, light detection andranging (PLADAR) system, the PLADAR system comprising: a laser to outputa laser beam; a collimator configured to collimate the laser beam on anaxis to emit a planar beam along a beam plane; and a detector array todetect reflected light based on the planar beam being reflected fromexternal surfaces of target objects, the detector array comprising aplurality of photodetectors arranged on a circuit board such that eachof the photodetectors is aligned with the beam plane.
 10. The autonomousvehicle of claim 9, wherein the laser comprises a fiber laser thatoutputs the laser beam.
 11. The autonomous vehicle of claim 9, whereinthe collimator comprises a set of mirrors that produce the planar beamfrom the laser beam.
 12. The autonomous vehicle of claim 9, wherein thecollimator comprises a set of lenses that produce the planar beam fromthe laser beam.
 13. The autonomous vehicle of claim 9, wherein theplurality of photodetectors include at least one row of photodetectors,and wherein the circuit hoard includes a number of adjustment componentsto calibrate each photodetector included in the at least one row ofphotodetectors.
 14. The autonomous vehicle of claim 13, the PLADARsystem further comprising: an adjustment controller that dynamicallyadjusts the adjustment components to configure the at least one row ofphotodetectors in response to a command signal.
 15. The autonomousvehicle of claim 13, further comprising: a control system that processessensor data from the PLADAR system to dynamically identify features asthe autonomous vehicle accelerates and maneuvers.
 16. The autonomousvehicle of claim 15, the PLADAR system further comprising: an analog todigital converter (ADC) chain coupled to the at least one row ofphotodetectors; wherein the control system of the C samples data fromeach ADC of the ADC chain simultaneously.